{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:43:37Z","timestamp":1784043817716,"version":"3.55.0"},"reference-count":70,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BPMJ"],"published-print":{"date-parts":[[2023,1,13]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Studies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>LSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Based on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>To the best of the authors\u2019 knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. 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